Machine learning for weather prediction increasingly relies on ensemble methods to provide probabilistic forecasts. Diffusion-based models have shown strong performance in Limited-Area Modeling (LAM) but remain computationally expensive at sampling time. Building on the success of global weather forecasting models trained based on Continuous Ranked Probability Score (CRPS), we introduce CRPS-LAM, a probabilistic LAM forecasting model trained with a CRPS-based objective. By sampling and injecting a single latent noise vector into the model, CRPS-LAM generates ensemble members in a single forward pass, achieving sampling speeds up to 39 times faster than a diffusion-based model. We evaluate the model on the MEPS regional dataset, where CRPS-LAM matches the low errors of diffusion models. By retaining also fine-scale forecast details, the method stands out as an effective approach for probabilistic regional weather forecasting

翻译：天气预报中的机器学习方法日益依赖集合预报以提供概率预测。基于扩散的模型在有限区域建模（LAM）中表现出强大性能，但在采样时计算成本仍然高昂。基于连续分级概率评分（CRPS）训练的全球天气预报模型取得成功的基础上，我们提出了CRPS-LAM，这是一种采用基于CRPS的目标函数训练的概率LAM预报模型。通过对模型采样并注入单个潜在噪声向量，CRPS-LAM在单次前向传播中生成集合成员，采样速度比基于扩散的模型快达39倍。我们在MEPS区域数据集上评估该模型，CRPS-LAM的误差水平与扩散模型相当。通过同时保留精细尺度的预报细节，该方法成为概率区域天气预报的有效方案。